<scp>HNA</scp> antibody‐mediated neutrophil aggregation is dependent on serine protease activity
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Transfusion-related acute lung injury (TRALI) is often caused by antibodies against human neutrophil alloantigen-2 (HNA-2) and HNA-3a. Neutrophil aggregation is considered as a major cause of TRALI, but little is known about how HNA antibodies initiate this process. We explored mechanisms involved in neutrophil aggregation induced by HNA-2 and HNA-3a antibodies. MATERIALS AND METHODS: Isolated neutrophils were pretreated with broad-spectrum or specific inhibitors against different cell functions or proteases. Granulocyte agglutination test (GAT) was performed with serially diluted anti-HNA-2 and anti-HNA-3a plasmas or control plasma, and reactivity was evaluated microscopically. Reactive oxygen species (ROS) production in neutrophils was investigated using a lucigenin-based chemiluminescence assay. RESULTS: HNA-2 and HNA-3a antibody-mediated neutrophil aggregation was inhibited by pretreatment with formaldehyde, iodoacetamide and the serine protease inhibitors Pefabloc-SC, N-p-tosyl-L-phenylalanine chloromethyl ketone (TPCK) and Nα-tosyl-L-lysine chloromethyl ketone hydrochloride (TLCK). In contrast, inhibition of actin polymerization, respiratory burst, cysteine proteases, metalloproteases or aspartic proteases did not affect neutrophil aggregation. Furthermore, HNA-3a antibodies did not directly cause ROS production in neutrophils. CONCLUSION: Aggregation of neutrophils induced by HNA-2 and HNA-3a antibodies is an active process and depends on trypsin- or chymotrypsin-like serine proteases but is not dependent on the production of ROS. These findings may open new prospects for the pharmacologic prevention of neutrophil-associated acute lung injury.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".